How do I install the SQL To Business Logic skill?

Run npx skillmds@latest add nimrodfisher/sql-to-business-logic in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is SQL To Business Logic safe to use?

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Which AI agents work with SQL To Business Logic?

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Is SQL To Business Logic free to use?

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Who published SQL To Business Logic?

nimrodfisher (@nimrodfisher) published this skill. Their other Agent Skills are listed on their SkillMD profile.

SQL To Business Logic by nimrodfisher

Name: SQL To Business Logic
Rating: 4.2 (5 reviews)
Author: nimrodfisher

File contents

SQL to Business Logic Translator

When to use

A stakeholder asks "what exactly does this query calculate?"
Documenting a query library or a dbt model for non-technical readers
Reviewing a query for correctness by comparing its logic to the business requirement
Onboarding new analysts to existing SQL patterns
Translating legacy undocumented queries before refactoring

Process

Receive the query and context — obtain the SQL and the business question it answers. Also collect any schema notes (what the key tables and columns represent in business terms).
Translate the FROM/JOIN structure — describe in plain language which data sources are combined and what type of join is used (inner keeps only matches; left keeps all rows from the left side). Note if the join type seems inconsistent with the stated purpose.
Translate WHERE filters — list each filter condition as a business rule in plain language (e.g., status = 'completed' → "only includes orders that have been paid and fulfilled").
Explain GROUP BY and aggregations — describe what each aggregation computes and at what grain. Use scripts/sql_explainer.py to automate a first-pass structural parse.
Summarise output columns — for each output column, state its business meaning and any edge cases (nulls, rounding, currency units).
Flag issues and write validation questions — identify potential problems (implicit null propagation, unexpected fan-out, hardcoded dates). Generate 3–5 questions the query author should confirm. Use assets/query_documentation_template.md to record the full translation.

Inputs the skill needs

The complete SQL query (SELECT through ORDER BY)
The business question the query is intended to answer
Table and column descriptions (or a data catalog entry)
Any business rules for key status values, date handling, or currency
The intended output: who reads the result and for what decision

Output

scripts/sql_explainer.py — parses a SQL query and generates a structured plain-language explanation
assets/query_documentation_template.md — completed translation covering purpose, step-by-step logic, output columns, business rules, and validation questions
Optionally: a flowchart representation of the query logic

---name: sql-to-business-logicdescription: Translate SQL queries into plain language business logic. Use when documenting queries, explaining analysis to non-technical stakeholders, code reviewing for correctness, or building a query catalog.---# SQL to Business Logic Translator# When to use- A stakeholder asks "what exactly does this query calculate?"- Documenting a query library or a dbt model for non-technical readers- Reviewing a query for correctness by comparing its logic to the business requirement- Onboarding new analysts to existing SQL patterns- Translating legacy undocumented queries before refactoring# Process1. **Receive the query and context** — obtain the SQL and the business question it answers. Also collect any schema notes (what the key tables and columns represent in business terms).2. **Translate the FROM/JOIN structure** — describe in plain language which data sources are combined and what type of join is used (inner keeps only matches; left keeps all rows from the left side). Note if the join type seems inconsistent with the stated purpose.3. **Translate WHERE filters** — list each filter condition as a business rule in plain language (e.g., `status = 'completed'` → "only includes orders that have been paid and fulfilled").4. **Explain GROUP BY and aggregations** — describe what each aggregation computes and at what grain. Use `scripts/sql_explainer.py` to automate a first-pass structural parse.5. **Summarise output columns** — for each output column, state its business meaning and any edge cases (nulls, rounding, currency units).6. **Flag issues and write validation questions** — identify potential problems (implicit null propagation, unexpected fan-out, hardcoded dates). Generate 3–5 questions the query author should confirm. Use `assets/query_documentation_template.md` to record the full translation.# Inputs the skill needs- The complete SQL query (SELECT through ORDER BY)- The business question the query is intended to answer- Table and column descriptions (or a data catalog entry)- Any business rules for key status values, date handling, or currency- The intended output: who reads the result and for what decision# Output- `scripts/sql_explainer.py` — parses a SQL query and generates a structured plain-language explanation- `assets/query_documentation_template.md` — completed translation covering purpose, step-by-step logic, output columns, business rules, and validation questions- Optionally: a flowchart representation of the query logic

Frequently asked questions

SQL To Business Logic